6 research outputs found
The drivers and predictability of wildfire re-burns in the western United States (US)
Evidence is mounting that the effectiveness of using prescribed burns as a management tactic may be diminishing due to the higher incidence of wildfire re-burns. The development of predictive models of re-burns is thus essential to better understand their primary drivers so that forest management practices can be updated to account for these events. First, we assess the potential for human activity as a driver of re-burns by evaluating re-burn trends both within and outside of the wildland–urban interface (WUI) of the western US. Next, we investigate the predictability of re-burns through the application of both random forest and the explanatory machine learning non-negative matrix factorization using k -means clustering (NMFk) algorithms to predict re-burn occurrence over California based on a number of climate factors. Our findings indicate that while most states showed increasing trends within the WUI when trends were conducted over longer moving windows (e.g. 20 years), California was the only state where the rate of increase was consistently higher in the WUI, indicating a stronger potential for human activity as a driver in that location. Furthermore, we find model performance was found to be robust over most of California (Testing F1 scores = 0.688), although results were highly variable based on EPA level III Ecoregion (F1 scores = 0.0–0.778). Insights provided from this study will lead to a better understanding of climate and human activity drivers of re-burns and how these vary at broad spatial scales so that improvements in forest management practices can be tuned according to the level of change that is expected for a given region
Climate-driven disturbances in the San Juan River sub-basin of the Colorado River
Accelerated climate change and associated forest disturbances in the
southwestern USA are anticipated to have substantial impacts on regional
water resources. Few studies have quantified the impact of both climate
change and land cover disturbances on water balances on the basin scale, and
none on the regional scale. In this work, we evaluate the impacts of forest
disturbances and climate change on a headwater basin to the Colorado River,
the San Juan River watershed, using a robustly calibrated (Nash–Sutcliffe efficiency
0.76) hydrologic model run with updated formulations that improve estimates
of evapotranspiration for semi-arid regions. Our results show that future
disturbances will have a substantial impact on streamflow with implications
for water resource management. Our findings are in contradiction with
conventional thinking that forest disturbances reduce evapotranspiration and increase
streamflow. In this study, annual average regional streamflow under the
coupled climate–disturbance scenarios is at least 6–11 % lower than
those scenarios accounting for climate change alone; for forested zones
of the San Juan River basin, streamflow is 15–21 % lower. The monthly
signals of altered streamflow point to an emergent streamflow pattern related
to changes in forests of the disturbed systems. Exacerbated reductions of
mean and low flows under disturbance scenarios indicate a high risk of low
water availability for forested headwater systems of the Colorado River
basin. These findings also indicate that explicit representation of land
cover disturbances is required in modeling efforts that consider the impact
of climate change on water resources